code: def _to_np_array(
      mp_arr: bytearray,
      dtype="int32",
      shape: Optional[Tuple[int, int]] = None,
  ) -> np.ndarray:  # pragma: no cover
      """multipropecessing Helper function to convert a multiprocessing
      RawArray to a numpy array."""
      arr = np.frombuffer(mp_arr, dtype=dtype)
      if shape is None:
          return arr
      return arr.reshape(shape)
  
imports: import numpy
  from sklearn.metrics import confusion_matrix
  import multiprocessing
  import sys
  import warnings
  from typing import Any, Dict, Optional, Tuple, List
  from functools import reduce
  import platform
  from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
  from cleanlab.rank import order_label_issues, get_label_quality_scores
  import cleanlab.internal.multilabel_scorer
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.util import value_counts_fill_missing_classes, round_preserving_row_totals, get_num_classes
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes, int2onehot
  from cleanlab.typing import LabelLike
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class
label_desc: Use image node_id 8 for calling a global function with example usage: _to_np_array(mp_arr, dtype, shape) and returns: arr, arr